[REQUEST] How to Monitor and Optimize GPU and CPU Resource Usage?

Hello everyone,

I have been training and fine-tuning large language models using DeepSpeed recently. During this process, I am looking to better understand and monitor the inter-GPU communication, the transfer of parameters and operators, as well as the usage of GPU memory and CPU memory.

Specifically, I am facing the following challenges:

How can I monitor the communication and data transfer in a multi-GPU environment?
What are the best practices to accurately measure and optimize the memory usage of the GPU in my model?
Are there tools or techniques that can help me better monitor the usage of CPU memory during training?
I have tried using nvidia-smi to observe GPU usage, but I find the information provided somewhat limited. I am looking for more detailed analysis, especially in a distributed training context.

The issue names no repository files, tests, or entry points. Start by reviewing DeepSpeed's distributed-training and memory-monitoring guidance, including the limitations of nvidia-smi. Done would require a clearly scoped, project-specific answer covering inter-GPU communication, GPU and CPU memory measurement, and optimization practices.